The Most Disruptive Year in Decades:
How and Why AR Will Evolve in 2026
Analyst Relations is entering one of its most disruptive years in decades. In 2026, the merging of agentic AI, new research formats and AI-first buyer behavior will push AR far beyond its regular “relationships plus deck” comfort zone.
This will affect AR budgets, the behavior of analyst firms, and lead to a further shift in how buyers access recommendations from third parties such as analysts … and GenAI models.
Vendor AR leaders will be expected to orchestrate intelligent co-pilots, govern how AI interacts with analyst content, and demonstrate their impact using real-time data on influence and research consumption, rather than relying on anecdotal wins.
Buying journeys are starting differently: Buyers are already beginning their journeys with AI assistants integrated into enterprise platforms instead of search engines or vendor sites, and analyst content is increasingly delivered directly into those environments through collaborations between major firms and hyperscalers. This shift fundamentally alters how analyst insights are discovered, interpreted and trusted.
Against this backdrop, Destrier’s seven predictions for 2026 examine how AR must evolve as analysts, buyers, vendors and AI co-pilots all share the same agentic research environment. The recent move by IDC to incorporate its intelligence into the AWS agentic AI research platform indicates that this future is no longer hypothetical; it is being integrated into mainstream enterprise workflows today.
AR co-pilots will be pervasive: Destrier envisions a world where co-pilots quietly operate in the background of every AR program; where real-time Analyst Influence Graphs replace static tiering; where “prompt-native” research is written as much for machines as for humans; and where emerging evaluation frameworks clash with established quadrants in fast-changing markets.
Looking ahead to 2026, this also signifies long-overdue transparency in research consumption analytics, board-level oversight of AI workflows, and spatial or physical‑AI-driven analyst experiences that reshape how vendors create briefings and events.
In essence, 2026 marks the year AI in AR starts becoming mainstream.
Internal AR co‑pilots will increasingly become standard tools for briefing preparation, in‑call note capture, action extraction and RFI orchestration.
In parallel, buyer‑side co‑pilots hosted by hyperscalers are moving from concept to reality, as shown by IDC’s decision to integrate its research and data into Amazon Quick Research on AWS, where agents can work directly over IDC intelligence alongside enterprise and web data.
This aligns with IDC’s broader assessment that AI is changing who makes buying decisions, with buyers increasingly beginning their research through AI-powered chat functions, while continuing to rely on human experts for trust and verification.
For AR, this means co‑pilots are no longer just internal productivity aids; they become the connective tissue between analyst content, answer engines and AI‑first buyer journeys.
The proliferation of co-pilots also increases the importance of source and fact-checking. This is a core part of any AR team’s submission process for analyst RFIs and will now include checking for both human and machine errors (or hallucinations).
AI will impact the end-to-end process of responding to analyst RFIs, as AR teams will increasingly expect analyst firms to prefill RFI responses using past submissions and public information, while also adopting Answer Engine Optimization (AEO) practices to ensure that their analyst coverage, proof points, and narratives are easy for LLMs to interpret, trust, and cite.
The new frontier is ensuring vendor narratives remain consistent across internal co‑pilots, analyst portals, and agentic AI environments like Amazon Quick Suite, where IDC content and other sources are already shaping what buyers see as the “best answer”.
In short, AI co-pilots will start transitioning from experimental tools to core infrastructure.
Innovative vendors will shift from static analyst tier lists to dynamic Analyst Influence Graphs (AIGs) that track how ideas and citations move across firms, coverage areas, communities and AI surfaces.
In 2026, leading AR teams will start using AIGs to map cross‑influence among analysts, social clusters and executive networks. They may also begin experimenting to determine which voices and vendors are most frequently surfaced as “best answers” in AI search and co‑pilot responses for mission‑critical queries.
However, this will lead to friction, and adoption will be uneven. Expect this to cast doubt over the validity of AIGs, at least until they become more sophisticated and more transparent.
AIGs show promise as they simulate how a single report, blog, quadrant move or media mention ripples through AI‑assisted discovery paths and into budget decisions, incorporating answer‑engine visibility as a first‑class signal.
Analyst firms and vendors whose content is not structured or distributed in ways that LLMs can easily interpret, trust and cite will find their influence diminished in AI‑first journeys, even if they retain strong brand recognition in traditional channels.
Back in February, we saw that AR was evolving from art into a science. Now, we’re witnessing widespread AI-driven automation.
Destrier expects that at least one top‑tier analyst firm will introduce “prompt native” research in 2026, focused on answer engines and co‑pilots.
This content will favor machine‑readable and indexable content so that LLMs can reliably extract, recombine, and attribute insights, in much the same way CCOs are now being urged to optimize corporate content for AI search. These principles reflect the emerging discipline of LLM Optimization (LLMO) and AEO (Answer Engine Optimization), and that means vendors will chase “share of model” alongside “share of voice”.
Prompt‑native research will sit on a human‑to‑machine content spectrum, balancing clear, emotionally resonant narratives for people with factual density and structure for machines.
Vendors that fail to reorganize their AR content into similarly modular, schema‑rich formats risk a visible decline in how often they are referenced or recommended in AI‑generated answers, even if they continue to perform well in formal evaluations and peer reviews.
Within a year, Destrier expects Gartner’s EMQ framework to encompass at least one non‑GenAI domain, such as AI observability, AI safety and governance, or agentic platforms, and to be routinely consumed through AI‑powered search and co‑pilots.
In these areas, vendors will increasingly see EMQ positioning show up in AI responses to buyer questions about “emerging leaders” or “innovators”. These citations may start challenging traditional Magic Quadrant citations in AI‑mediated decision flows.
As AI answer engines blend Magic Quadrants, Waves, EMQs and thought leadership into synthesized recommendations, the practical distinction between “emerging” and “mainstream” frameworks will blur.
AR leaders will need playbooks that treat EMQs and MQs as inputs to AI‑first experiences, making sure their key proof points and differentiation are expressed in AEO‑friendly ways so that answer engines surface the right evaluative signals at the right moments in the buying journey.
Destrier still considers full-fidelity research consumption analytics aspirational, but the trajectory is becoming clearer. Instrumented analytics will expand from portal usage and inquiry counts to include answer‑engine visibility and sentiment.
Today, licensing, privacy and compliance restraints stand in the way of AI “eating the world” when it comes to analyst firm data. Nevertheless, we are seeing analyst research, vendor content and earned media all increasingly consumed through AI search interfaces.
In response, AR leaders will track how often their brand and competitors appear in AI answers to a curated set of mission‑critical questions, mirroring the AEO monitoring approaches now being recommended to CCOs.
In anticipation, AR programs will allocate budgets and ownership for tooling and processes that combine analyst-firm telemetry (where available) with AI search monitoring and internal pipeline data.
The resulting dashboards will show which reports, topics, and frameworks are read and cited. They will also gauge how AI systems position the vendor. Vendors will track what URLs are being pulled, which analyst notes are referenced and where competitors gain disproportionate visibility in AI‑generated summaries. What’s still missing? Source weighting.
As analyst research and vendor narratives are ingested into general‑purpose AI search and co‑pilot experiences, governance and “analyst safety” become not just contractual concerns but also trust and reputation issues.
With more than half of consumers already expressing low confidence in AI‑generated summaries, any distortion, bias or hallucination involving analyst content or vendor claims can rapidly undermine credibility.
Destrier expects at least one high‑profile incident in 2026 in which an AI‑mediated interpretation of analyst research triggers scrutiny from customers, regulators, or the media, accelerating top‑down pressure for robust AI governance around analyst workflows.
AR leaders will need clear policies and controls for how LLMs access, summarize, and remix analyst IP; how AI‑generated outputs that reference analysts are validated; and how misrepresentations in AI search or co‑pilot answers are detected and escalated.
Finally, vendors will start introducing strict controls mandating that a “human in the loop” isn’t just a nice-to-have but an essential requirement before signing off on an AI-completed response to an analyst RFI. This will help counter any over-reliance on AI, which allows mistakes to slip through.
Traditional slide‑centric analyst days will increasingly be complemented by multimodal, AI‑first experiences that span both physical and digital spaces. Forward‑looking vendors will use spatial and immersive environments such as digital twins, simulations and on‑site demonstrations of AI‑controlled systems to give analysts a tangible understanding of complex operations.
At the same time, generating rich, modular content such as Q&A, walkthroughs and structured explainers will feed AI search and answer engines.
Destrier expects post‑event surveys in 2026 to show that immersive and AI‑augmented formats significantly improve perceived understanding of the solution and roadmap compared to traditional briefings.
At least one major analyst firm is likely to publish a case study positioning these spatial and answer‑engine‑aware formats as best practice for explaining complex operational technology. The success of an event will be measured not only by the room experience, but also by how well its outputs perform in AI‑mediated discovery afterward.
About Destrier
We’re focused on industry analyst relations, enterprise peer‑review program management and executive advisory services. We help in‑house AR and marketing teams build influence, gain recognition and achieve measurable impact on pipeline and revenue through innovative, data‑driven strategies.
Known for our emphasis on power‑law dynamics and rigorous metrics, Destrier works with ambitious AR teams to reboot or scale their programs, concentrating effort where it will move the needle most. Established in 2016, Destrier offers services including strategic AR advisory and interim leadership, end‑to‑end management of major evaluations, launches and events, plus fully outsourced peer‑review programs that consistently support five‑star category positions.
Vendors of all sizes call on Destrier to translate complex technology into compelling narratives, embed AR best practices and help executives navigate the “dark arts” of analyst influence in an AI‑driven buying landscape.
If you’d like to know more, get in touch!